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Efficient Few-shot Learning for Pixel-precise Handwritten Document Layout Analysis by De Nardin, Axel; Zottin, Silvia; Paier, Matteo; Foresti, Gian Luca; Colombi, Emanuela; Piciarelli, Claudio is a scholarly article available to read on EtoBox.

What is Efficient Few-shot Learning for Pixel-precise Handwritten Document Layout Analysis about?

Layout analysis is a task of uttermost importance in ancient handwritten document analysis and represents a fundamental step toward the simplification of subsequent tasks such as optical character recognition and automatic transcription. However, many of the approaches adopted to solve this problem rely on a fully supervised learning paradigm. While these systems achieve very good performance on this task, the drawback is that pixel-precise text labeling of the entire training set is a very time-consuming process, which makes this type of information rarely available in a real-world scenario. In the present paper, we address this problem by proposing an efficient few-shot learning framework that achieves performances comparable to current state-of-the-art fully supervised methods on the publicly available DIVA-HisDB dataset.

Author

De Nardin, Axel; Zottin, Silvia; Paier, Matteo; Foresti, Gian Luca; Colombi, Emanuela; Piciarelli, Claudio

Published

2022

Language

EN